DePoint: Improving rotation robustness of 3D point cloud analysis via decreasing entropy

Abstract

In real-world scenarios, achieving rotation robustness in point cloud analysis is crucial due to the unpredictable orientations of 3D objects. While recent advancements in rotation robustness typically rely on auxiliary modules to align rotated objects, precisely aligning object orientations remains challenging given the vast space of possible rotations. In this work, we investigate the impact of rotation on point clouds, revealing that random rotations significantly increase the joint entropy of point clouds and semantic labels—a key factor leading to degraded model performance on rotated datasets. To address this issue, we introduce DePoint, a simple yet effective rotation enhancement method that decreases entropy by aligning the spatial distribution of rotated point cloud representations with semantic information. Specifically, a Siamese point cloud encoder processes differently oriented views of an object with a shared task head, ensuring semantic consistency in the learned representations. A minimal auxiliary classifier enforces linear separability into these representations. Notably, DePoint can be seamlessly integrated into existing point cloud models without introducing additional parameters during inference. Experimental results demonstrate that DePoint significantly enhances the rotation robustness of various point cloud models in 3D object classification and segmentation.